Bayesian vs Frequentist MMM: What Meridian Gets Right

Ask a courtroom witness what happened and a jury how confident they are in the verdict, and you get two different kinds of answer to what feels like one question. The witness gives a single account. The jury gives a judgement shaped by everything they already believed walking in, weighed against the evidence they just heard.

Marketing mix modelling has run its own version of this split for years, and it has a name: frequentist versus Bayesian.

The difference isn't academic. It changes how much you should trust the number your model hands you, and what questions to ask before you spend a budget on it.

The older, more conventional way of building an MMM, called a frequentist model, looks purely at the historical data and hands back one single best-fitting answer for how much each channel contributed. Meta's Robyn was originally built this way.

A Bayesian model does something different: it starts from an existing belief about how likely a channel's effectiveness is, called a prior, then updates that belief as it sees the data, and hands back not one number but a range of plausible answers along with how confident it actually is in them. Google's Meridian is built entirely this way.

That range matters more in MMM than it does in most fields, because marketing data is often thin and channels frequently move up and down together, which is exactly the situation where a single confident-looking "best fit" number is least trustworthy. A Bayesian model is more honest about that uncertainty instead of hiding it behind one tidy figure.

Meridian's real contribution is letting real experiments talk to the model

Google released Meridian to everyone in early 2025 as the successor to its earlier, more limited MMM tool. It models at the geo level by default, meaning it wants marketing and sales broken out by region rather than as one national total, and it supports the two most common ways of describing how an ad's effect fades over time, called geometric and binomial decay.

The feature genuinely worth the attention it's gotten is calibration: Meridian can take the result of a real incrementality experiment and fold it directly into the model, tightening its estimate for that specific channel around evidence that actually happened, rather than a pattern the model merely inferred from the data. That's the single biggest practical reason to prefer a Bayesian tool like Meridian: it has a built-in, structured way to absorb a real test result, rather than treating the test as a separate fact you have to reconcile with the model yourself.

Practitioners who've used both tools also point to Meridian being built in Python rather than Robyn's R as a real accessibility win for most teams, as fluency in Python is much more common.

A Bayesian model is only as honest as the belief you feed into it

The part of Meridian most worth being skeptical about is exactly the part that makes it powerful: the prior. Independent reviews are consistent that running Meridian well genuinely needs solid data expertise, and that setting a defensible starting belief is hard even for people who do this for a living.

That matters commercially, not just technically: a prior can end up quietly shaping the answer before the data ever gets a say, and an inflated one can be chosen, deliberately or not, to produce a more flattering result.

None of this makes the Bayesian approach the wrong choice. It's a genuine improvement on hiding uncertainty behind one tidy number. What it changes is where your due diligence needs to go: not "is the model statistically sound" but "where did these starting assumptions come from, and were they earned by real evidence or just convenient." If whoever built your model can't answer that plainly, that's the actual red flag worth chasing, not the choice of tool itself.

Deciding whether a Bayesian model like Meridian is the right fit, and setting priors that reflect real evidence rather than convenience, is exactly the kind of judgement call worth having an outside pair of eyes on. Get in touch with Day1data before you commit to a set of priors.

FAQ

What's the difference between Bayesian and frequentist MMM? A frequentist model produces a single best-fitting estimate from the data alone. A Bayesian model combines the data with a starting belief, called a prior, and produces a range of plausible answers with its confidence attached, rather than hiding that uncertainty behind one number.

What is Google Meridian? Google's open source, fully Bayesian marketing mix modelling tool, released to general availability in early 2025.

Why does the Bayesian approach suit MMM particularly well? Because marketing data is often thin, with channels frequently scaled up or down together, which makes a single "best fit" answer unreliable. A starting belief gives the model something more stable to lean on in exactly those conditions.

What does Meridian do with incrementality test results? It folds a real experiment's result directly into the model, tightening its estimate for that specific channel around evidence that actually happened, rather than a pattern the model only inferred.

Does Meridian support the same decay options as Robyn? Not quite. Meridian currently supports two common decay shapes; Robyn supports a third, more flexible one better suited to channels with a slow-building effect, like TV. Neither gap is a dealbreaker on its own.

What's the main risk of a Bayesian MMM? The starting belief itself. One chosen without solid evidence behind it can dominate the result, especially with limited historical data, and there's a real risk of it being set, deliberately or not, to produce a more flattering answer.

Is Meridian accessible for a team without a data scientist? Not easily. Independent reviews are consistent that it needs real statistical expertise to run and check properly, and its documentation is written for a technical audience rather than an executive one.

What should you actually ask before trusting a Bayesian model's output? Where the starting beliefs came from, and whether they were grounded in real evidence, like an incrementality test, or just chosen to make a channel look good. If nobody can answer that clearly, treat the output with caution regardless of how sophisticated the model looks.